Detail publikace

Evolutionary Approximation in Non-Local Means Image Filters

VÁLEK, M. SEKANINA, L.

Originální název

Evolutionary Approximation in Non-Local Means Image Filters

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The non-local means image filter is a non-trivial denoising algorithm for color images utilizing floating-point arithmetic operations in its reference software implementation. In order to simplify this algorithm for an on-chip implementation, we investigate the impact of various number representations and approximate arithmetic operators on the quality of image filtering. We employ Cartesian Genetic Programming (CGP) to evolve approximate implementations of a 20-bit signed multiplier which is then applied in the image filter instead of the conventional 32-bit floating-point multiplier. In addition to using several techniques that reduce the huge design cost, we propose a new mutation operator for CGP to improve the search quality and obtain better approximate multipliers than with CGP utilizing the standard mutation operator. Image filters utilizing evolved approximate multipliers can save 35% in power consumption of multiplication operations for a negligible drop in the image filtering quality.

Klíčová slova

Cartesian genetic programming, image filter, approximate multiplier, automated design, mutation

Autoři

VÁLEK, M.; SEKANINA, L.

Vydáno

9. 12. 2022

Nakladatel

Institute of Electrical and Electronics Engineers

Místo

Praha

ISBN

978-1-6654-5258-8

Kniha

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Strany od

2759

Strany do

2766

Strany počet

8

BibTex

@inproceedings{BUT179617,
  author="Matěj {Válek} and Lukáš {Sekanina}",
  title="Evolutionary Approximation in Non-Local Means Image Filters",
  booktitle="2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
  year="2022",
  pages="2759--2766",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Praha",
  doi="10.1109/SMC53654.2022.9945091",
  isbn="978-1-6654-5258-8"
}